{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emotion Classification in Texts using Scikit-learn \n",
    "\n",
    "Classifying short messages into five emotion categories. We will prepare our dataset (nltk and regular expressions) and vectorize words using TF-IDF (term frequency-inverse document frequency) metric. Later we will use classifiers provided by scikit-learn and classify sentences into five emotion categories: joy, sadness, anger, fear, and neutral.\n",
    "\n",
    "### Workflow \n",
    "* Importing Dataset\n",
    "* Text Preprocessing\n",
    "* Text Representation\n",
    "* Classifiers: Naive Bayes, Linear Regression, Random Rorrrest, SVM\n",
    "* Evaluation: F1 scores and Confussion Matrix\n",
    "* Saving the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# text preprocessing\n",
    "from nltk import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "\n",
    "# plots and metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "# feature extraction / vectorization\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "# classifiers\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# save and load a file\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Dataset\n",
    "\n",
    "Text-Emotion Dataset was split into training 70% and testing 30%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of training set: 7934\n",
      "size of validation set: 3393\n",
      "joy        2326\n",
      "sadness    2317\n",
      "anger      2259\n",
      "neutral    2254\n",
      "fear       2171\n",
      "Name: Emotion, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Emotion</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>There are tons of other paintings that I thin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sadness</td>\n",
       "      <td>Yet the dog had grown old and less capable , a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fear</td>\n",
       "      <td>When I get into the tube or the train without ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fear</td>\n",
       "      <td>This last may be a source of considerable disq...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anger</td>\n",
       "      <td>She disliked the intimacy he showed towards so...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emotion                                               Text\n",
       "0  neutral   There are tons of other paintings that I thin...\n",
       "1  sadness  Yet the dog had grown old and less capable , a...\n",
       "2     fear  When I get into the tube or the train without ...\n",
       "3     fear  This last may be a source of considerable disq...\n",
       "4    anger  She disliked the intimacy he showed towards so..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('data/data_train.csv')\n",
    "df_test = pd.read_csv('data/data_test.csv')\n",
    "\n",
    "X_train = df_train.Text\n",
    "X_test = df_test.Text\n",
    "\n",
    "y_train = df_train.Emotion\n",
    "y_test = df_test.Emotion\n",
    "\n",
    "class_names = ['joy', 'sadness', 'anger', 'neutral', 'fear']\n",
    "data = pd.concat([df_train, df_test])\n",
    "\n",
    "print('size of training set: %s' % (len(df_train['Text'])))\n",
    "print('size of validation set: %s' % (len(df_test['Text'])))\n",
    "print(data.Emotion.value_counts())\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Plotting confusion matrix for later evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize=False,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues):\n",
    "    '''\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    '''\n",
    "    if not title:\n",
    "        if normalize:\n",
    "            title = 'Normalized confusion matrix'\n",
    "        else:\n",
    "            title = 'Confusion matrix, without normalization'\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    # Set size\n",
    "    fig.set_size_inches(12.5, 7.5)\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    ax.grid(False)\n",
    "    \n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Text Preprocessing\n",
    "\n",
    "Here are some preprocessing steps to consider:\n",
    "* Removing noise: html markups, urls, non-ascii symbols, trailing whitespace etc.\n",
    "* Removing punctuation\n",
    "* Normalizing emoticons\n",
    "* Negation handling\n",
    "* Tokenization: split text into word tokens\n",
    "* Stopword removal\n",
    "* Stemming or lemmatization\n",
    "\n",
    "However, most of these steps did not improve our classification results. Since our data was mostly taken from written dialogs it was almost ready to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_and_tokenize(data):    \n",
    "\n",
    "    #remove html markup\n",
    "    data = re.sub(\"(<.*?>)\", \"\", data)\n",
    "\n",
    "    #remove urls\n",
    "    data = re.sub(r'http\\S+', '', data)\n",
    "    \n",
    "    #remove hashtags and @names\n",
    "    data= re.sub(r\"(#[\\d\\w\\.]+)\", '', data)\n",
    "    data= re.sub(r\"(@[\\d\\w\\.]+)\", '', data)\n",
    "\n",
    "    #remove punctuation and non-ascii digits\n",
    "    data = re.sub(\"(\\\\W|\\\\d)\", \" \", data)\n",
    "    \n",
    "    #remove whitespace\n",
    "    data = data.strip()\n",
    "    \n",
    "    # tokenization with nltk\n",
    "    data = word_tokenize(data)\n",
    "    \n",
    "    # stemming with nltk\n",
    "    porter = PorterStemmer()\n",
    "    stem_data = [porter.stem(word) for word in data]\n",
    "        \n",
    "    return stem_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Text Representation\n",
    "\n",
    "Vectorizing text using Term Frequency technique (Term Frequency(TF) — Inverse Dense Frequency(IDF))\n",
    "* Tekenize with our preprocess_and_tokenize\n",
    "* Find it’s TF = (Number of repetitions of word in a document) / (# of words in a document)\n",
    "* IDF = log(# of documents / # of documents containing the word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TFIDF, unigrams and bigrams\n",
    "vect = TfidfVectorizer(tokenizer=preprocess_and_tokenize, sublinear_tf=True, norm='l2', ngram_range=(1, 2))\n",
    "\n",
    "# fit on our complete corpus\n",
    "vect.fit_transform(data.Text)\n",
    "\n",
    "# transform testing and training datasets to vectors\n",
    "X_train_vect = vect.transform(X_train)\n",
    "X_test_vect = vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Classifiers\n",
    "\n",
    "###  Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 67.02%\n",
      "\n",
      "F1 Score: 67.02\n",
      "\n",
      "COnfusion Matrix:\n",
      " [[469  32  44  28 120]\n",
      " [ 73 420  55  16 115]\n",
      " [ 56  18 475  68  90]\n",
      " [ 61  20  76 385  96]\n",
      " [ 68  20  48  15 525]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb = MultinomialNB()\n",
    "\n",
    "nb.fit(X_train_vect, y_train)\n",
    "\n",
    "ynb_pred = nb.predict(X_test_vect)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy_score(y_test, ynb_pred) * 100))\n",
    "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, ynb_pred, average='micro') * 100))\n",
    "print(\"\\nCOnfusion Matrix:\\n\", confusion_matrix(y_test, ynb_pred))\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plot_confusion_matrix(y_test, ynb_pred, classes=class_names, normalize=True, title='Normalized confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Random Forrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.72%\n",
      "\n",
      "F1 Score: 63.72\n",
      "\n",
      "COnfusion Matrix:\n",
      " [[396  87  47  83  80]\n",
      " [ 66 444  50  75  44]\n",
      " [ 68  62 433  95  49]\n",
      " [ 34  22  50 507  25]\n",
      " [ 94  84  62  54 382]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=50)\n",
    "rf.fit(X_train_vect, y_train)\n",
    "\n",
    "yrf_pred = rf.predict(X_test_vect)\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy_score(y_test, yrf_pred) * 100))\n",
    "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, yrf_pred, average='micro') * 100))\n",
    "print(\"\\nCOnfusion Matrix:\\n\", confusion_matrix(y_test, yrf_pred))\n",
    "\n",
    "plot_confusion_matrix(y_test, yrf_pred, classes=class_names, normalize=True, title='Normalized confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 69.35%\n",
      "\n",
      "F1 Score: 69.35\n",
      "\n",
      "COnfusion Matrix:\n",
      " [[456  67  44  68  58]\n",
      " [ 65 483  42  50  39]\n",
      " [ 56  34 476 101  40]\n",
      " [ 41  23  42 498  34]\n",
      " [ 82  60  51  43 440]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=200)\n",
    "log.fit(X_train_vect, y_train)\n",
    "\n",
    "ylog_pred = log.predict(X_test_vect)\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy_score(y_test, ylog_pred) * 100))\n",
    "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, ylog_pred, average='micro') * 100))\n",
    "print(\"\\nCOnfusion Matrix:\\n\", confusion_matrix(y_test, ylog_pred))\n",
    "\n",
    "plot_confusion_matrix(y_test, ylog_pred, classes=class_names, normalize=True, title='Normalized confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Linear Support Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.71%\n",
      "\n",
      "F1 Score: 72.71\n",
      "\n",
      "COnfusion Matrix:\n",
      " [[490  49  41  58  55]\n",
      " [ 53 508  34  40  44]\n",
      " [ 50  33 498  91  35]\n",
      " [ 34  23  38 505  38]\n",
      " [ 72  43  53  42 466]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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YkY53Sm+zfNlbHHxwbTp0PLryA0wC5erjEtrk5+fz5huvs2DxWxxcuzZDB55Cl4wMTux3ctziDaKf3McYK1cso1r16rz90Ua+/PILhp7WjxNOPInmLVrGLd6gqsi5vHL5UqpXr8Y76z7hyy+/IOvUfvTtd7L6WZKjIhZ1PPCMu4fd/VPgNaC7u78GtDazw4DRwHR3zy/6YXf/p7t3c/duVrNO1UZegtzPdpDeaG8FLNTwEPI+31Fs2xF9WjNl4bqqCi1ppIVC5G7eVLCcl7uZJk2aFm6TtrdNfn4+X321vdC/ZGdMnczwszQsWZJQKL1QH+fm5tIkLa1Qm9g/h9g+TguFOL7PCTRs1IjatWtz6oCBrFm1qkrjD4Kmaenkbt5csLwlL5cmTZuW2CY/P5+vo308Y8okTup/KjVq1KBx48PocWxv1qxaUaXxB0VaWoi83L39nJebS5MmZZzL2yP9PH3qJE7uf1pBP/c8ther1c8/jaGhyf1UaTXFJ4BzgPOBR6omnIpbvu5/tE6rz5GHH0qNlGqMPKE1c5fm7NOuTag+qXVqseSDT6s+yIDLyOzOhvXZbMz5mF27djFj2hQGDB5SqM3AwUOY9NQTAMx8djp9+vYr+Ffw7t27mfnsdIaNUCJWkoxu3VmfnU3Onj6eOplBRfp40OChPP3U4wA8N2MaJ0T7+ORTTuOdd9by7bffkp+fz+JFC2nXvn0iDmO/1jWzGxs27D2Pn50+hdMGZRVqc9qgLCY/EzmPZz83neP7noiZEWrWjMULF+Du7NixgxXL3qJ126MScRj7va5FrhfPTp/MgMGF+3nAoCwmPR3p51nP7b1epKcfwaLX/lPQz8uXLaWN+llIrqHJhcBFZvYY0AA4AfhldNujwFLgv+7+bmLC+/HCu52r/76I2b/Lono147GXP+D9T77ghnO6s3Ld1oKk7KwTWjN1UfY+n3/5tjNom16fOgfVIPuR87j4/gW8vGrTPu0OZCkpKdx+132MOH0Q4XCYc8aOp32Hjvzx9zfRNaMbAwcP4dxxE7j4gnFkdjqK1NRU/v3Y0wWff2PxQtJCIQ0vlCIlJYU777mfYUMGEg6HOXfc+bTv0JE/3HITXTMyGZQ1lPPGT2DihLF06diW1NQGPPxEpI9TU1O57Iqr6Hd8T8yMU04byGkDByf4iPY/KSkp3HbHvZx95mDC4d2MOW8c7dp35LZbb6ZLRiYDBg3hnLHnc+nE8fTo3J7U1FT+8ciTAEy48BKu/PkFnNCzC+7OqHPH0fHoYxJ7QPuplJQUbrvzPkaeMZjdu8OMOW887dp35E+33kyXrpkMHDyEc8ZO4OcXjqd753bUT03lX488BcCEiZdwxSUXcHyPSD+PVj9XgCXVDV2tuPHsoDGzr4G6wO3AQMCBW919ckybF4Dn3P3vZe2vWv0jvVaf6+IVrgB5kycmOoSkV71a8jyLbX+184dwokM4IFRLoucK7o9OPqEnq1euCEwnR/6O/nVc9v39nEtXuHu3uOy8BIGviJlZQ+Bzj2SUv2RvFSy2TW2gDfBMFYcnIiIilS2JkvNA1/bMLA14E7izlDb9gQ+AB9x9e1XFJiIiInGSoMn6ZjbAzD40s2wz22fozMzuMbPV0ddHZvZlWfsMdEXM3fOAtmW0eRk4omoiEhERkWRkZtWBB4FTgM1EbpM1y90LHq/g7lfHtL8c2PfZhEUEuiImIiIiB6DE3NC1B5Dt7hvcfRcwCTi9lPajKceUKCViIiIiIhGNzGx5zCv2l2UhIPbWA5uj6/ZhZkcCLYBXy/rCQA9NioiIyAHG4nr7is9K+dVkcSWzkm49MQqY5u5l/rRaFTERERGRsm0GmsUspwN5JbQdRTnv1KCKmIiIiARLYm5fsQxoY2YtgFwiydaYoo3M7CgglchdHcqkRExEREQCpejD1quCu+eb2WXAfKA68LC7v2tmtwDL3X1WtOloYJKX8475SsREREREysHd5wHziqy7scjyzT9mn0rEREREJDCMxFTE4kWT9UVEREQSRBUxERERCQ6j+BtJBJQqYiIiIiIJooqYiIiIBIgl1RwxJWIiIiISKMmUiGloUkRERCRBVBETERGRQFFFTEREREQqTBUxERERCRRVxERERESkwlQRExERkeBIshu6KhETERGRwLAku4+YhiZFREREEkQVMREREQkUVcREREREpMJUERMREZFASaaKmBIxERERCZRkSsQ0NCkiIiKSIKqIiYiISHAk2X3EVBETERERSRBVxERERCRQNEdMRERERCpMFTEREREJjGR7xJESMREREQmUZErENDQpIiIikiCqiImIiEiwJE9BTBUxERERkURRRUxERESCw5JrjpgSsWIc07IRrzxzYaLDSGppA29NdAhJb+uLNyQ6hKRXq0b1RIdwQNjtnugQkpol0zhfACkRExERkUBRRUxEREQkQZIpEdNkfREREZEEUUVMREREAiPZ7qyvipiIiIhIgqgiJiIiIsGSPAUxVcREREREEkUVMREREQkO3dBVREREJHGSKRHT0KSIiIhIgqgiJiIiIoGiipiIiIiIVJgqYiIiIhIsyVMQU0VMREREJFFUERMREZFASaY5YkrEREREJDDM9KxJEREREakEqoiJiIhIoKgiJiIiIiIVpoqYiIiIBIoqYiIiIiIHGDMbYGYfmlm2mV1XQpuzzOw9M3vXzJ4ua5+qiImIiEiwJKAgZmbVgQeBU4DNwDIzm+Xu78W0aQP8BjjO3b8ws8PK2q8SMREREQmUBA1N9gCy3X1DNIZJwOnAezFtLgQedPcvANz9f2XtVEOTIiIiIhGNzGx5zGtizLYQsClmeXN0Xay2QFsze93MlpjZgLK+UBUxERERCQ6La0XsM3fvVvI378OLLKcAbYATgXRgkZkd7e5flvSFqoiJiIiIlG0z0CxmOR3IK6bNTHf/wd0/Bj4kkpiVSImYiIiIBIYBZvF5lWEZ0MbMWphZTWAUMKtIm+eAfgBm1ojIUOWG0naqoUkREREJkMQ8a9Ld883sMmA+UB142N3fNbNbgOXuPiu67VQzew8IA790922l7VeJmIiIiEg5uPs8YF6RdTfGvHfgF9FXuSgRExERkUBJohvra46YiIiISKKoIiYiIiKBomdNioiIiEiFqSImIiIiwVG+W00EhhIxERERCQwDqlVLnkxMQ5MiIiIiCaKKmIiIiARKMg1NqiImIiIikiCqiImIiEig6PYVIiIiIlJhSsT2c6+8NJ+eXTvSvXM77rvr9n2279y5k5+NG0P3zu04tV9vPtmYA8APP/zApRPPp0/PLvTK7MS9d/65iiMPjlN6tGLNE5fyzlOXc+2Y4/bZfvulp7Hk3xex5N8X8faTl7Flzq8Ltn3z6g0F26b+cVRVhh0oL734Al07tadzh7bcdce+5+LOnTsZd+4oOndoS78+vdiYkwPAxpwcGtc/hN49MujdI4MrL7ukiiMPjpdffIHMY9rTpWNb7i6hj8efO4ouHdtyUp9ebIxeKwDeWfs2/fseR8+MTvTq1pnvv/++CiMPjpdffIHunTuQcfRR3FPMNXXnzp1MOG80GUcfRf8TehVcjyHSx6eeeBy9Mo+hd/cu6uOKiN6+Ih6vRNjvhibNrDkwx92PTnAoCRcOh/n1NVcwbebzpIXSOaXvsQwYnMVR7ToUtHnq8YepX78+y9Z8wIxpk/ndjf/HQ489zcxnp7Fz1y4WvbWab7/9luO6H8OwkWdzxJHNE3dA+6Fq1Yx7rxrE4GueIHfrVyz+x4XMef1DPtj4WUGbXz04v+D9JcN60LlNk4Ll73bmc+wF/6jSmIMmHA5zzZWXM3PufELp6fQ9rieDs4bQrv3e8/jxRx+mfv1U1rz3EdOmTOLG317HY09OAqBFy1a8sXRlosIPhHA4zDVXXc5zc+cTCqXT7/ieDCquj1NTWf1upI9vuv46Hn1yEvn5+UycMJZ/PPQYnY7pzOfbtlGjRo0EHs3+KRwO88urr+DZOS+QFkrnpD7HMnBw4T5+4tGHqVc/lZXvfMj0qZO5+be/4eEnniE/P5+LfjaOv//7UfVxJTA0NClVZOXypbRo2YrmLVpSs2ZNzhx+Ns/PmV2ozfNzZzNqzHkADD1jOIsWvIq7Y2Z8u2MH+fn5fP/dd9SoUZNDD62biMPYr3VvH2J97ufkbPmSH/J3M/XVd8k6vl2J7c86+WimvPJOFUYYfMuXLaVlq1a0aBk5j4ePPJs5s2cVajN39kzGnDsWgDOGjWDBfyLnsZTPij19HL1WDBt5NnPnFO7jeXNmMuacvX38WvRa8erLL9Lx6E50OqYzAA0aNqR69epVfgz7uxXLI32853o8bMRZzCvSx8/PncXocyPX49PPHK4+lnKJWyJmZoeY2VwzW2Nm75jZ2WZ2o5ktiy7/06IprZllRtu9CVwas4/xZjbDzF4ws3VmdnvMtlPN7E0zW2lmU82sTnT9bWb2npm9bWZ3RteNjH7nGjNbGK9jrmxbtuSRFkovWE4LhdiyJbdwm7w8QunNAEhJSaFuvXp8vm0bQ88YTu1DDqFj62Z06dCSS6+4mtQGDao0/iBIa3Qom//3VcFy7tavCDU6tNi2RxxejyOb1mfByo8L1h1UM4XF/7iQ1/76M4Ycf1Tc4w2iLXm5BecoQCgUYkte4fM4Ly+P9JjzuF7demzbtg2AjTkfc1zPTAb078frixdVXeABkldcH+eWca2oG7lWZK9bh5lx5pAB9OnVjXvvuqNKYw+KLXl5hEJ7+zgtlM6WvLxCbfJi2sT28frsSB8PHzqQvr26c9/d6uOKMczi80qEeA5NDgDy3H0wgJnVA15y91uiy08AWcBs4BHgcnd/zcyKnqFdgK7ATuBDM3sA+A74LdDf3XeY2a+BX5jZX4AzgXbu7mZWP7qPG4HT3D03Zl0hZjYRmAiQ3uyISuqCiimuIlD0RCmpzcrlS6levRrvrPuEL7/8gqxT+9G338k0b9EybvEGUXH/45VUhxl50tE899r77N69t0Xbs+5hy7ZvaN60Pi/cM453NvyPj/O+iFO0wVSR87hJ06a8ty6Hhg0bsmrlCkaPHMbSVWupW1fV3VgV6eP8/HzefON1Fix+i4Nr12bowFPokpHBif1Ojlu8QVSePqaUPl7yxuu8umgJB9euzRmDTqFL1wz6qo+F+A5NrgX6m9mfzayPu28H+pnZW2a2FjgJ6BhN0Oq7+2vRzz1RZD+vuPt2d/8eeA84EjgW6AC8bmargXHR9V8B3wP/NrNhwLfRfbwOPGpmFwLF1oPd/Z/u3s3duzVs1KiSuqBi0tJC5OVuLljOy82lSZO0wm1CIXI3bwIgPz+fr7ZvJ7VBA6ZPncTJ/U+jRo0aNG58GD2P7cXqVSuqNP4gyN36FemH7f1LPdS4LnmffV1s2xEnd2TKy2sLrduy7RsAcrZ8ycLVOXSJmT8mEWmh9IJzFCA3N5cmTQufx6FQiM0x5/H2r7bToEEDatWqRcOGDQHompFJi5atyF73UdUFHxCh4vo4rYxrxVeRa0VaKMTxfU6gYaNG1K5dm1MHDGTNqlVVGn8QpIVC5Obu7eO83M00adq0xDaF+zid42L6+JTTBrJmtfq4IpJpsn7cEjF3/wjIJJKQ/cnMbgT+Coxw907Av4CDiMy7K20yyM6Y92EiVTwjUl3rEn11cPefuXs+0AOYDpwBvBCN5WIiFbRmwGoza1iJhxo3XTO7s2F9NhtzPmbXrl08O30yAwZnFWozYFAWk56O5K6znptOn779MDPS049g0Wv/wd3ZsWMHy5ctpU1bDZ0VtfyDXFqnN+TIJvWpkVKNkSd1ZO7rH+7Trk2zhqTWOZgl7+5NjOvXOYiaNSJ5fcN6B9OrUzPez9laZbEHRWa37qzPzibn48h5PH3qZAZnDSnUZlDWUJ5+8nEAnpsxjb4nRs7jrVu3Eg6HAfh4wwbWr1+nqm4xMvb0cfRaMWPqZAYNLtLHg4fy9FN7+/iE6LXi5FNO45131vLtt9+Sn5/P4kULade+fSIOY7+WkRnp4z3X4xnTpjCwSB8PGDSEZ56MXI9nPjt9bx/3P5V31+7t49cXL+SodupjiYjb0KSZpQGfu/uTZvYNMD7EsWMAAAAgAElEQVS66bPofK4RwDR3/9LMtpvZ8e6+GDinHLtfAjxoZq3dPdvMagPpQB5Q293nmdkSIDsaSyt3fwt4y8yGEEnItlXm8cZDSkoKt915HyPPGMzu3WHGnDeedu078qdbb6ZL10wGDh7COWMn8PMLx9O9czvqp6byr0eeAmDCxEu44pILOL5HF9yd0eeOo+PRxyT2gPZD4bBz9b3zmH3nuVSvZjw2bzXv52zlhgknsvKDPOa+Eam+nHXy0Ux9tfAk/XZHNuKBa7PYvdupVs2486nXC/3aUiJSUlK48977OWPIQHaHw5w37nzad+jIrb+7ia6ZmQzOGsrY8RO4cMJYOndoS2qDBjzy+NMAvLF4IbfecjMpKSlUr16dex/4Kw0013EfKSkp3HnP/QwbMpBwOMy50T7+wy030TUjk0FZQzlv/AQmThhLl45tSU1twMNPRPo4NTWVy664in7H98TMOOW0gZw2cHCCj2j/k5KSwu1338fwoYMIh8OcM3Y87Tt05I+33ESXjG4MyhrCeeMncPHPxpFx9FGkpqbyUPQ8rp+ays+vuIqT+xwLZpxy2gD1cQUl068mLV6/TDKz04A7gN3AD8AlRKpUo4AcYBOw0d1vNrNM4GEiQ4nziVTNjjaz8UA3d78sus85wJ3uvsDMTgL+DNSKfuVvgWXATPZW2u5098fMbAbQJrruFeAqL+XAu2Rk+isL36q0vpB9pQ/+Q6JDSHpbX7wh0SEkvd36YWeV2K1f0MZVv+N6smrl8sBkNrVDR3m7i/4Wl32vuunkFe7eLS47L0HcKmLuPp9IUhVrOZGEqWjbFUDnmFU3R9c/Cjwa0y4r5v2rQPdivrpHMfsfVu7ARURERKrIfndDVxEREZGS6IauIiIiIlIpVBETERGRQEmigpgqYiIiIiKJooqYiIiIBEoyzRFTIiYiIiKBkkR5mIYmRURERBJFFTEREREJDkuuoUlVxEREREQSRBUxERERCYzIDV0THUXlUUVMREREJEFUERMREZEAsaSaI6ZETERERAIlifIwDU2KiIiIJIoqYiIiIhIoyTQ0qYqYiIiISIKoIiYiIiLBYZojJiIiIiKVQBUxERERCYzIDV2TpySmRExEREQCJZkSMQ1NioiIiCSIKmIiIiISKElUEFNFTERERCRRVBETERGRQEmmOWJKxERERCQ4dB8xEREREakMqoiJiIhIYBiWVEOTqoiJiIiIJIgqYiIiIhIoSVQQU0VMREREpDzMbICZfWhm2WZ2XTHbx5vZVjNbHX1dUNY+VRETERGRQKmWgJKYmVUHHgROATYDy8xslru/V6TpZHe/rLz7VSImIiIigZKgockeQLa7b4jEYJOA04GiidiPoqFJERERkYhGZrY85jUxZlsI2BSzvDm6rqjhZva2mU0zs2ZlfaEqYiIiIhIYZnG9s/5n7t6tpK8uZp0XWZ4NPOPuO83sYuAx4KTSvlAVMREREZGybQZiK1zpQF5sA3ff5u47o4v/AjLL2qkqYiIiIhIo1RIzR2wZ0MbMWgC5wChgTGwDM2vq7luii0OB98vaqRIxERERkTK4e76ZXQbMB6oDD7v7u2Z2C7Dc3WcBV5jZUCAf+BwYX9Z+lYiJiIhIoCTqEUfuPg+YV2TdjTHvfwP85sfsU4lYMQyjWoLqngeKz166sexGUiGNRv4j0SEkvfceGp/oEA4IjQ+tmegQkloQ71IfxJhLosn6IiIiIgmiipiIiIgEhhEZuUoWqoiJiIiIJIgqYiIiIhIoyTSNWxUxERERkQRRRUxERESCwyxht6+IByViIiIiEihJlIdpaFJEREQkUVQRExERkcAwoFoSlcRUERMRERFJEFXEREREJFCSqCCmipiIiIhIoqgiJiIiIoGi21eIiIiIJICZhiZFREREpBKoIiYiIiKBottXiIiIiEiFqSImIiIigZI89TBVxEREREQSRhUxERERCRTdvkJEREQkASLPmkx0FJWnxETMzOqW9kF3/6rywxERERE5cJRWEXsXcArPiduz7MARcYxLREREZF9mB8bQpLs3q8pARERERA405frVpJmNMrP/i75PN7PM+IYlIiIiUrw9jzmq7FcilJmImdlfgH7AedFV3wJ/j2dQIiIiIgeC8vxqsre7Z5jZKgB3/9zMasY5LhEREZFiHRBzxGL8YGbViEzQx8waArvjGpWIiIhIMZLt9hXlmSP2IDAdaGxmvwMWA3+Oa1QiIiIiB4AyK2Lu/riZrQD6R1eNdPd34huWiIiISPEOtKFJgOrAD0SGJ/V8ShEREZFKUJ5fTV4PPAOkAenA02b2m3gHJiIiIlIci9MrEcpTETsXyHT3bwHM7A/ACuBP8QxMREREpCgzqJZEQ5PlGWbcSOGELQXYEJ9wRERERA4cpT30+x4ic8K+Bd41s/nR5VOJ/HJSREREpMolUUGs1KHJPb+MfBeYG7N+SfzCERERETlwlPbQ74eqMhARERGR8jigbl9hZq2APwAdgIP2rHf3tnGMS0RERCTplWey/qPAI0R+2TkQmAJMimNMIiIiIiUyi88rEcqTiNV29/kA7r7e3X8L9ItvWCIiIiL7MoxqFp9XIpQnEdtpkcHY9WZ2sZkNAQ6Lc1wS9fKLL9CjSwcyOx3FvXfu+4jPnTt3MmHsaDI7HUX/vr34ZGMOAFMnPc0Jx2YWvBrWqcHaNaurOPpgeGn+C3Q9uh3HtG/DXXfcts/2nTt3MvacURzTvg0nHn8sG3NyANiYk0OjerXp1b0rvbp35YpLL67iyIPjlK7NWPPXUbzz99FcO7zLPttv/1lvltwzgiX3jODtv45iy1PnF2w7p19b1v5tNGv/Nppz+mlGRElee+VFTj72GPp178jf7rtjn+1L31jMkJN60aZJHebNmlGwPnfTRoae3JvBJ/bktOMzeOrRf1Vl2IHy0osv0LVTezp3aMtddxR/PR537ig6d2hLvz69Cl0rGtc/hN49MujdI4MrL7ukiiOX/Vl5buh6NVAHuILIXLF6wIR4BiUR4XCYX/3iCmbMfoG0UDon9zmWAYOH0K59h4I2Tz72MPXrp7Ji7YdMnzqZm2/4DQ8//gwjR41h5KgxALz3zlrOOXsYnTrv+xfggS4cDvOLKy9j1rwXCaWnc0LvHgzKGkr7mD5+7JGHqF+/Pm+/v46pUyZxw/XX8fhTkdH5Fi1b8eayVYkKPxCqVTPuveh4Bt80h9xtO1h85zDmLN3IB5u+KGjzq4feKHh/yeCj6dyyEQCpdWpx/ahuHHfNdNydN+4ewdylOXy5Y1eVH8f+LBwOc9N1V/H41Lk0SQtxxqnH039AFm2Oal/QJi29Gbc/8E/+/dd7C3228eFNmTrvP9SqVYsd33zDgBMy6T9gMIc3Savqw9ivhcNhrrnycmbOnU8oPZ2+x/VkcFbh6/Hjj0aux2ve+4hpUyZx42+v47En914r3li6MlHhJ5cEDiPGQ5kVMXd/y92/dvdP3P08dx/q7q9XRXDxYBGBeF7miuVLadGyFc1btKRmzZoMG3EWz8+ZVajNvDmzGHXOeQCcfuZwFi54FXcv1Gb61EkMH3l2lcUdJMuXLaVlq9a0aBnp4xFnnc3c2TMLtZk7exbnnDcOgDOHjWDBf17Zp4+lZN3bHMb6/35Fzqdf80P+bqYuWk9Wj+Yltj/rhNZMWZgNRCppr6zezBff7OTLHbt4ZfVmTs04oooiD441K5dxZPNWHNG8BTVr1iTrjJG89PycQm3SjziS9h07Ua3I5a9mzZrUqlULgF27drJ79+4qiztIIteKVgXXiuEjz2bO7MLX47mzZzLm3LEAnDFsBAv+s+/1WKSoEhMSM3vWzGaU9KrsQMzsOTNbYWbvmtnE6LpvzOwPZrbGzJaY2eHR9a2iy8vM7BYz+yZmP7+Mrn/bzH4XXdfczN43s78CK4FmlR1/PGzJyyOUvjfUtFA6W7bkldgmJSWFunXr8fm2bYXaPDt9KsNGjop/wAGUl5dLerP0guVQKJ283Nx928T0cb269dgW7eONOR/Tu0cGp/U/kdcXL6q6wAMkreEhbP6s4H9Rcrd9Q6jhIcW2PaJxHY487FAWrM0t8bNpJXz2QPbfLXk0De09j5umhfh0S24pnygsL3cTA/t257gubbjo8mtUDSvGlrzcQtfjUCjElryi14q8Uq8Vx/XMZED/frpWVAIzi8srEUobmvxLlUURMcHdPzezg4FlZjYdOARY4u7Xm9ntwIXArcB9wH3u/oyZFUzMMbNTgTZADyK/8pxlZicAnwBHAee7+8+L+/Jo8jcRIL3Z/vEv7uL+JVX0RHFKb7N82VscfHBtOnQ8uvIDTALl6uMS2jRp2pT3szfSsGFDVq1cwaiRZ7Js1TvUrVs3bvEGUXGXtpKqBCP7tOa5Nzawe3dke3HXRVUYilGO87g0aaFmPP/aMj79bx4XjT2LgUPOpPFhh1dmhIFX0WvFe+tyCq4Vo0cOY+mqtbpWCFBKRczdXyntFYdYrjCzNUTu3N+MSEK1C9hTX18BNI++7wVMjb5/OmYfp0Zfq4hUvtpF9wOw0d1LfCqAu//T3bu5e7dGjRpX/GgqQVooRO7mTQXLebmbadKkaeE2aXvb5Ofn89VX20lt0KBg+4ypkxl+loYlSxIKpbN50+aC5dzczTRNS9u3TUwfb/9qOw0aNKBWrVo0bNgQgK4ZmbRo2YrsdR9VXfABkbttB+mN6hQshxrWIe/zb4ttO6JPa6Ysyt772c/2/eyWEj57IGuSFmJL7t7zeEteLof9hKrW4U3SaHNUB5YtCezsk7hJC6UXuh7n5ubSpGnRa0VI14oqUi1Or0TYL+ZKmdmJQH+gl7t3JpJIHQT84Hv/iRGm7B8XGPAnd+8SfbWOeULAjjiEHlcZmd3ZsD6bjTkfs2vXLmZMm8KAwUMKtRk4eAiTnnoCgJnPTqdP334F/0rbvXs3M5+dzrARSsRKktmtO+uz15HzcaSPp02ZzKCsoYXaDMoawlNPPAbAszOm0ffEkzAztm7dSjgcBuDjDRtYn72O5i1aVvkx7O+Wr/sfrZvW48jDDqVGSjVG9mnF3KU5+7RrE6pH6iG1WPLBpwXrXlq1if5d06l/SE3qH1KT/l3TeWnVpn0+e6A7pms3cj7OZtPGHHbt2sWc56bSf8Dgcn12S95mvv/uOwC2f/kFK5a+ScvW+nVqUZFrRXbBtWL61MkMzip8PR6UNZSnn3wcgOdmTKPvif2Kv1as17WiIowDZ2iyKtUDvnD3b82sHXBsGe2XAMOByUDs5Kf5wO/N7Cl3/8bMQsAPcYm4CqSkpHD7Xfcx4vRBhMNhzhk7nvYdOvLH399E14xuDBw8hHPHTeDiC8aR2ekoUlNT+fdjewuEbyxeSFoopP/hS5GSksJd9z7AGVkDCIfDnDf+fDp06Mjvf3cjGRndGDxkKOPO/xkXnD+WY9q3IbVBAx594hkAXl+8kFt/dxMpKSlUr16d+x74Gw1iqpESEd7tXP3Pxcy+eTDVqxmPvfIh72/6ghvGdGNl9lbmLt0IwFl92jB1cXahz37xzU7+NHkFi+8aDsAfJ6/gi292Vvkx7O9SUlK4+U/3MO6sIezeHWbk6HG0bdeBe267hU5dMug/IIs1q5Zzybiz2b79S155cR733X4r8xevJPujD/njTddhZrg7F156Fe06aCpDUSkpKdx57/2cMWQgu8Nhzht3Pu07dOTW391E18xMBmcNZez4CVw4YSydO7QltUEDHnk8cj1+Y/FCbr3l5oJrxb0P/FXXCilg5Z1vYWa13D0uV0AzqwU8B4SAD4HGwM3AHHevE20zAshy9/Fm1gZ4kkhiPBeY6O6haLsrgQuiu/4GOJdINW2Ou5fr6tI1o5u/uvitSjo6KU7NlP2iGJvUGo38R6JDSHrvPTQ+0SEcEBofWjPRISS1E3r3YOWK5YG5IcThrY/20XdNi8u+7zuj/Qp37xaXnZegzL8NzayHma0F1kWXO5vZA5UZhLvvdPeB7n6Mu4909xPdfcGeJCzaZpq7j48u5gLHunsPIonb8ph297l7p+irV/RpADnlTcJEREREimNmA8zsQzPLNrPrSmk3wszczMpM6sozNHk/kEWkYoW7rzGzRD/iKBP4S/SO/1+iG8yKiIgcMKoloH5nZtWBB4FTgM1E7vAwy93fK9LuUCI3wS/X0Fp5ErFq7r6xyCS2cLmijhN3XwR0TmQMIiIickDpAWS7+wYAM5sEnA68V6Td74HbgWvLs9PyTNTZZGY9ADez6mZ2FaDf3YqIiEiVM4vrryYbmdnymNfEmK8OAbE/294cXRcTm3UFmrl74UdblKI8FbFLiAxPHgF8CrwcXSciIiJS5eI4NPlZKZP1i70/dcHGyOMT7wHG/5gvLDMRc/f/UfgWESIiIiIHms0UfkRiOhD73MFDgaOBBdHqWhMiT/gZ6u7LKUGZiZiZ/Qv2fY6Ou08sprmIiIhIXCXo3qvLgDZm1oLI3RtGAWP2bHT37UCjPctmtgC4trQkDMo3NPlyzPuDgDMpPEYqIiIiktTcPd/MLiNy8/jqwMPu/q6Z3QIsd/dZP2W/5RmanBy7bGZPAC/9lC8TERERqQgDqiWoJObu84B5RdbdWELbE8uzz59ye/MWwJE/4XMiIiIiEqM8c8S+YO8csWrA50CJd5MVERERiadkekheqYlY9M71nYlMSgPY7eV9OKWIiIhIHCRoZDIuSk0qo0nXs+4ejr6UhImIiIhUkvL8anKpmWW4+8q4RyMiIiJSCjNL2GT9eCgxETOzFHfPB44HLjSz9cAOIj9YcHfPqKIYRURERJJSaRWxpUAGcEYVxSIiIiJSpiQqiJWaiBmAu6+volhEREREDiilJWKNzewXJW1097vjEI+IiIhIqeL40O8qV1oiVh2oQ/FPGxcRERGpcom8s348lJaIbXH3W6osEhEREZEDTJlzxERERET2J0lUECv1hq4nV1kUIiIiIgegEiti7v55VQYiIiIiUiY7cCbri4iIiOx3LIlmTyXTA8xFREREAkUVMREREQmMyO0rEh1F5VFFTERERCRBVBETERGRQFFFTEREREQqTBUxERERCRRLoju6KhETERGRwNBkfRERERGpFKqIiYiISHDYgfOsSRERERGJI1XEREREJFCqJVFJTBUxERERkQRRRUxEREQCI9l+NalETERERAIliUYmNTQpIiIikiiqiBXDDGpUT6J0ez9UPZnqyvupTydPTHQISe/wXlckOoQDwqdv3p/oEJKaJzqAH82oRvL8HaKKmIiIiEiCqCImIiIigWFojpiIiIiIVAJVxERERCQ4TLevEBEREUkY3VlfRERERCpMFTEREREJDE3WFxEREZFKoYqYiIiIBIrmiImIiIhIhakiJiIiIoGSRAUxJWIiIiISHEZyDecl07GIiIiIBIoqYiIiIhIcBpZEY5OqiImIiIgkiCpiIiIiEijJUw9TRUxEREQkYVQRExERkcAwkuuGrkrEREREJFCSJw3T0KSIiIhIuZjZADP70Myyzey6YrZfbGZrzWy1mS02sw5l7VOJmIiIiASKWXxepX+nVQceBAYCHYDRxSRaT7t7J3fvAtwO3F3WsSgRExERESlbDyDb3Te4+y5gEnB6bAN3/ypm8RDAy9qp5oiJiIhIgFg8b+jayMyWxyz/093/GX0fAjbFbNsM9NwnOrNLgV8ANYGTyvpCJWIiIiIiEZ+5e7cSthWX/e1T8XL3B4EHzWwM8FtgXGlfqERMREREAiOBD/3eDDSLWU4H8kppPwn4W1k71RwxERERCRQzi8urDMuANmbWwsxqAqOAWUXiahOzOBhYV9ZOVRETERERKYO755vZZcB8oDrwsLu/a2a3AMvdfRZwmZn1B34AvqCMYUlQIiYiIiIBk6gburr7PGBekXU3xry/8sfuU0OTIiIiIgmiipiIiIgEhxHP21dUOSViIiIiEhgJ/NVkXCTTsYiIiIgEiipiIiIiEijJNDSpipiIiIhIgqgiJiIiIoGSPPUwVcREREREEkYVMREREQmUJJoipkRMREREgiNy+4rkycQ0NCkiIiKSIKqIiYiISKAk09CkKmL7uZdefIGundrTuUNb7rrjz/ts37lzJ+POHUXnDm3p16cXG3NyANiYk0Pj+ofQu0cGvXtkcOVll1Rx5MHx4vwXOKbjUXRs15o7br9tn+07d+7k3DFn07Fda/r07lnQx3t88sknNKpfh3vuvrOKIg6el198gcxj2tOlY1vuLuE8Hn/uKLp0bMtJfXqxcWNOwbZ31r5N/77H0TOjE726deb777+vwsiD45Te7Vnz7A28M/Mmrj3/lH22337NMJZMuo4lk67j7eduZMvC2wu23XrF6Syf+n8sn/p/jDg1oyrDDhSdxxIPgayImVlzoLe7P/0TPvuNu9ep9KDiIBwOc82VlzNz7nxC6en0Pa4ng7OG0K59h4I2jz/6MPXrp7LmvY+YNmUSN/72Oh57chIALVq24o2lKxMVfiCEw2GuuuJS5j7/EqH0dI4/tjtZWUNp32FvHz/68EOk1k/l3Q+ymTJ5Etf/36958unJBdt/de3VnDpgYCLCD4RwOMw1V13Oc3PnEwql0+/4ngwq7jxOTWX1u5Hz+Kbrr+PRJyeRn5/PxAlj+cdDj9HpmM58vm0bNWrUSODR7J+qVTPuve4sBl/yF3I//ZLFT/2SOa+t5YMN/y1o86u7ZhS8v2RUXzoflQ7AgOM70qV9M3qOuo1aNVJ48aGrmP/6e3y9Q4lCLJ3H+xPDNEcs4ZoDY4rbYGaBTC6Ls3zZUlq2akWLli2pWbMmw0eezZzZswq1mTt7JmPOHQvAGcNGsOA/r+LuiQg3kJYtXUqrVq0L+njk2aOYM3tmoTZzZs/knPPGATBs+AgWvPpKQR/PmvkcLVq0pEOHjlUee1Cs2HMet4j08bCRZzN3TuHzeN6cmYw5Z+95/NqCyHn86ssv0vHoTnQ6pjMADRo2pHr16lV+DPu77kc3Z/2mz8jJ3cYP+WGmzl9J1onHlNj+rAGZTHlhBQDtWzZh0Yp1hMO7+fb7Xaz9aDOn9m5fVaEHhs5jiZcqTcTMrLmZvW9m/zKzd83sRTM72MxamdkLZrbCzBaZWbto+0fNbETM57+Jvr0N6GNmq83sajMbb2ZTzWw28KKZ1TGzV8xspZmtNbPTq/I4K8uWvFxC6c0KlkOhEFvycgu1ycvLIz3aJiUlhXp167Ft2zYANuZ8zHE9MxnQvx+vL15UdYEHSF5ebkH/AYRC6eTmFu3jXNKb7e3juvUifbxjxw7uuuPPXH/DTVUac9DkFXceF+njLXl5BW1SUlKoW7cen2/bRva6dZgZZw4ZQJ9e3bj3rjuqNPagSDusHps//aJgOffTLwg1rlds2yOapnJkWkMWLPsQgLc/yuW04zpw8EE1aFj/EPp2a0t6k9QqiTtIdB7vX8zi80qERFSP2gCj3f1CM5sCDAfOBy5293Vm1hP4K3BSKfu4DrjW3bMAzGw80As4xt0/j1bFznT3r8ysEbDEzGZ5KaUiM5sITARo1uyIih9lJSgu3KLP1yqpTZOmTXlvXQ4NGzZk1coVjB45jKWr1lK3bt24xRtEFenj3//uJi6/8mrq1AnESHfCVKSP8/PzefON11mw+C0Orl2boQNPoUtGBif2Ozlu8QZRccM0JV3sRp6WyXOvrGb37kiLV5Z8QGbHI/nPo9fw2Rff8NbbH5OfvzuO0QaTzuP9h25fUXEfu/vq6PsVRIYZewNTzWw18A+g6U/Y70vu/nn0vQF/NLO3gZeBEHB4aR9293+6ezd379aoceOf8PWVLy2UTu7mTQXLubm5NGmaVqhNKBRic7RNfn4+27/aToMGDahVqxYNGzYEoGtGJi1atiJ73UdVF3xAhELpBf0HkJu7mbS0on2czuZNe/v4q+2RPl629C2u/82vOKp1c/5y/73ccdsf+duDf6nS+IMgVNx5XKSP00Khgjb5+fl89dV2Uhs0IC0U4vg+J9CwUSNq167NqQMGsmbVqiqNPwhy//cl6YfvrWKFDk8lb+v2YtuOOC2TKS8sL7Tu9ofmc+yo28i65C+YGdmb/hfXeINI57HESyISsZ0x78NAA+BLd+8S89ozQSGfaIwW+adHzVL2uyPm/TlAYyDT3bsAnwIHVdYBVJXMbt1Zn51Nzscfs2vXLqZPnczgrCGF2gzKGsrTTz4OwHMzptH3xH6YGVu3biUcDgPw8YYNrF+/juYtWlb5MezvunXvTnb2uoI+njp5EoOzhhZqMzhrKE898RgAM6ZPo2+/kzAzXlmwiA+z/7+9+w6zqrr6OP79wYBKUVpUGBQbioDSrRFs9CLWWCASiNgLRvOaxKhBE301sb3GxBI1GhMVRUWxd2yRIojGBgpKMUrsNJ1hvX+cg7mMAwxlOJzL7/M883jLnnP33W7uXbP2PuvM4O1pMzjltDM4+5xfcuLJp2TxNtZrHZfO4xnJGI8edSd9+laYx30H8Pfb/zuPu3ZL5vEB3Xvy+utTWbBgAWVlZTw/7jla7ez9SxVNeGMmO2z9A1o0a0ytkpoc3rMjY5957XvtWrbYnIab1uHlKe9/91iNGqLRZnUBaNuyGW1bNuOJl95aZ33PC8/j9Ug1LUtuSEuTFX0JvC/p8IgYlQZcu0bEFGAG0Am4CzgIWHqayVdA/RUcczPg44j4VtJ+QItq6301Kikp4fdXXs3A/r1ZUl7O4GN/ws6t23DRb86nQ6dO9O03gB8PGcpxQ39Mu9Y70rBRI26+NTmR9MXnn+OikRdQUlJCzZo1ufL/rqVRo0YZv6P1T0lJCVdcdQ39+/akvLycY4cMpXWbNoy84Dw6dupMv/4DGDJ0GEOHDKZNqx1o2LARt91+R9bdzpWSkhJ+f8XVHNK/N+Xl5QxK5/FvR55Ph3L6eNcAACAASURBVI6d6NNvAIOHDGX40B/Tvs2ONGzYiJtuS+Zxw4YNOeW0M9jvh7sjie49e9Ozd9+M39H6p7x8CSP+9y4euPZkatYQf73/Zd587yN+fWJfJv3rA8Y+OxWAI3p1ZtSjE5f53VolNXnipjMA+OrrRQz91V8pL/fSZEWex1ZdtC7PsEvLTjwYEW3T+2cB9YC/An8iWZKsBdwRESMlbQHcT5IVexI4NSLqSaoFPAI0AW4BPgM6R8Qp6XGbAA+kx5oM7A30jogZVSlf0bFT53juxVfW5lu3Ckpq5vWE3fz4xvt8qt0We56WdRc2CP9+6eqsu1DUuu29G69OnJCbTVc7tm0f14x6vFqO3bP15hMjonO1HHw51mlGLCJmAG0L7hdWwOxVSft/A3sUPPSL9PFvgYq7HG8p+L15JJv3K+uDd1abmZnZemF9WJo0MzMzq7JiKujqQMzMzMxyQ0CN4onDcltZ38zMzCz3nBEzMzOzXCmmpUlnxMzMzMwy4oyYmZmZ5UpWxVergzNiZmZmZhlxRszMzMxypZj2iDkQMzMzs9xw+QozMzMzWyucETMzM7McUVEtTTojZmZmZpYRZ8TMzMwsP+TyFWZmZma2FjgjZmZmZrlSRAkxB2JmZmaWH0n5iuIJxbw0aWZmZpYRZ8TMzMwsV4onH+aMmJmZmVlmnBEzMzOzfCmilJgzYmZmZmYZcUbMzMzMcqWYLnHkQMzMzMxypYiqV3hp0szMzCwrzoiZmZlZrhRRQswZMTMzM7OsOCNmZmZm+VJEKTEHYmZmZpYborjOmvTSpJmZmVkVSOol6W1J0ySdU8nzZ0r6l6TXJD0pqcXKjulAzMzMzPJDSfmK6vhZ4ctKNYE/Ar2B1sBRklpXaPYq0DkidgXuBi5d2dtxIGZmZma2crsB0yLivYj4BrgDOKiwQUQ8HREL0rsvA81XdlDvETMzM7NcqcYdYk0kTSi4f31EXJ/eLgU+LHhuFrD7Co41DHh4ZS/oQMzMzMwsMS8iOi/nucriv6i0oTQI6Ax0W9kLOhAzMzOzfMnmpMlZwFYF95sDcyo2knQg8CugW0QsXtlBHYiZmZlZjiir8hXjgZaStgVmA0cCRy/TM6kDcB3QKyI+rspBvVnfzMzMbCUiogw4BXgUeBO4KyLekDRS0oC02WVAPWCUpMmSxqzsuM6ImZmZWa6srNREdYmIh4CHKjx2XsHtA1f1mM6ImZmZmWXEGTEzMzPLDVFUl5p0IFaZ8iXBFwvLsu5GUau7Uc2su1D0yssrPava1qKPXrwq6y5sELYcdEvWXShqi96bl3UXNmgOxMzMzCxfiigl5kDMzMzMciWj8hXVwpv1zczMzDLijJiZmZnlSlblK6qDM2JmZmZmGXFGzMzMzHKliBJizoiZmZmZZcUZMTMzM8uPIqvo6kDMzMzMcsXlK8zMzMxsjTkjZmZmZrkhXL7CzMzMzNYCZ8TMzMwsV4ooIeaMmJmZmVlWnBEzMzOzfCmilJgDMTMzM8sVl68wMzMzszXmjJiZmZnlistXmJmZmdkac0bMzMzMcqWIEmLOiJmZmZllxRkxMzMzy5ciSok5EDMzM7PcEC5fYWZmZmZrgTNiZmZmlh9y+QozMzMzWwucETMzM7NcKaKEmDNiZmZmZllxRszMzMzypYhSYg7EzMzMLEfk8hVmZmZmtuacETMzM7NccfkKMzMzM1tjzoiZmZlZboii2qvvQMzMzMxypogiMS9NmpmZmWXEGTEzMzPLFZevMDMzM7M15oyYmZmZ5YrLV9g68/QTj9K1S1v27rgz11xx2feef/mFcfTqtjstmtThwftHL/PcMYf1o3WLzTn2RwPXVXdz6YnHHqFLu9Z0bLsTV/z+f7/3/OLFixk6+Cg6tt2JA7vuyQczZ3z33OtTX6PHvnuzZ6dd2atLexYtWrQOe54fTz7+KLt3aEOXdq246g+Xfu/5xYsXM+zYo+nSrhU99tvruzH+9ttvOXn4T9hn9/bs2WkXrqzk/48lnnjsETq3a02HFczjnww+ig5td+KArnsys8I87r7v3uzhebxC3duXMvmqQ5n6f4fxs4G7VtrmkD23ZeIVBzPhioO5+fRu3z1+4aDOjL/8YMZffjCH7rXtuuqy5UCuAjFJp0l6U9LtWfdlXSgvL+fcs0/ntlFjePrlKdx/z52889aby7Qp3WorLv/jjQw87Mjv/f6Jp57JVX++aV11N5fKy8s5e8RpjLrvQV6eNJV7Rt3JW2/+a5k2t91yE5s1aMik19/mxFPP4IJzfwFAWVkZxw87lj9cfS0vTXyNBx95klq1amXxNtZr5eXl/M/PTuPO0Q/wwvjXGH33Hbz91rJjfPutN9GgQQPGT3mLE04+nd+c90sA7r/3bhZ/8w3j/jmZJ8f9k7/efMMygbAlysvLOWvEadx934P8c9JU7l7OPG7QoCGvvv42J1WYx8OHHcvlV1/Ly57Hy1Wjhrjip3sy8LeP0XHEaA7/4Xa0at5gmTbbb7kpZx+yKwecO5bOI+7l5zf/E4BeHZvTftvG7HHWfXT7xQOMOGgX6m/iMV4TqqafLOQqEANOAvpExDGrewBJNddif6rV5Inj2Wa77WmxzXbUrl2bgw45gsceemCZNlttvQ2t2+5CjRrf/1/5w277U7d+/XXV3VyaOOEVttt+e7bZNhnjQw47goceHLNMm4fHjuGoQYMBOOjgQ3n2maeICJ564jHatN2FXXZtB0Cjxo2pWTM302udmTThFbbd7r9jfPChP+LhB5edxw+PfYAjj07GeMDAQxmXjrEkFsyfT1lZGYsWLqRWrdrUr79pFm9jvVZxHh9ayTx+aAXzuK3n8Up13qEJ0z/6khkff8W3ZUu4+4X36Ndl62Xa/OTAHbnukTf5fP43AHzyZZJZbNW8Ac//6yPKlwQLFpcxdcandG/ffJ2/h6KhZGmyOn6ykJtATNKfge2AMZJ+JekmSeMlvSrpoLTNNpLGSZqU/uyVPr6vpKcl/R2YmuHbWCVz586haelW393fslkpc+fOzrBHxWfunDmUFoxxs9LmzJ0zZ5k2cwralJSUsOmmm/Hpf/7D9GnvIolDB/Sm255duOry7y8dWzKPm5X+90unWen35/HcOXMobV4wxpslYzxg4KHUqVuXNjtsRfvW23HyaSNo2KjROu1/HlRlHs9dzjyeNu1dkDhkQG+6eh4vV7NGdZk9b/5392f/Zz7NGtVZpk3LZpuxQ9NNefKivjzzu350b18KwNSZn9KjQ3M2qV2TxvU3omvbpjRvUned9t/WX7nZrB8RJ0jqBewHnAk8FRFDJTUAXpH0BPAx0D0iFklqCfwD6JweYjegbUS8X9nxJQ0HhgOUNt+6sibrXsT3HlIx7VBcD0RVxng5bcrKynj5xRd4atzLbFKnDgP7dKd9h4502++A6upuLlVljJfXZtKEV6hZswavv/sBn3/+Gf167Ee3/Q5gm223q7b+5lFl41fxz/vljXF5Oo+fTufxQZ7Hlarso7fikJbUFDs03Yye5z9EaeO6PHFhXzqPuJcnp8yh0/Y/4Onf9uOTLxfxz3c+pqx8ybrpeNEqnu/C3GTEKugBnCNpMvAMsDGwNVALuEHSVGAU0Lrgd15ZXhAGEBHXR0TniOjcuEmT6uv5KmjarJS5sz/87v5Hc2az5ZbNMuxR8WlWWsrsgjGeM3sWWzZtutw2ZWVlfPnlFzRs1Ihmpc3Ze5+uNG7ShDp16tC9Z2+mTH51nfY/D5o1K2XO7Fnf3Z8z+/vzuFlpKbNnFYzxF8kY3zPqDg44sCe1atXiBz/YnN332JPJr05cp/3Pg8rmcVPP47Vq9n/mU1qQxSptXJe5ny2o0GYBD46fSVl5MPPjr3lnzhfs0DRZSr909BT2OPt++l/4KAKmz/1yXXbf1mN5DcQEHBoR7dOfrSPiTWAE8G+gHUkmrHbB78yv5DjrtXYdO/P+9Gl8MPN9vvnmG+4ffRfde/fLultFpWOnLkyfNo2ZM5IxHn33XfTu23+ZNr369Ocff7sNgPvvvYeu3fZDEgcc2IM3pk5lwYIFlJWV8cLzz7FTq52zeBvrtQ6duvDe9P+O8b333EmvvsvO4159+nHH35MxHnPfPeyTjnHz5lsz7tmniQjmz5/PhPGv0HLHnbJ4G+u1pfN4RjrG91Qyj3t7Hq+RidPmsUPTzWixeT1qldTgsL23Y+z4D5Zp88ArM+naNgmAG9ffiJZNN+X9f39FjRqiUb2NAGjboiFtWzTiiSneZrK6RHHtEcvN0mQFjwKnSjo1IkJSh4h4FdgMmBURSyQdC+R6x2lJSQkXXnolxxzajyXl5fzomCHstHNrLvvdb2jXviM9+vRn8qQJ/HTwEXzx+Wc8/shYLr9kJE+9NBmAQ3rvz7R332b+/K/p3GY7fn/1n9n3gB4Zv6v1S0lJCZdefhWHDuhDeXk5x/x4CDu3bsPvRp5P+46d6dOvP4OHDOWEYcfSse1ONGzYkL/c+ncAGjRsyEmnncEB++wBEt179qJn774Zv6P1T0lJCZf8/ioOH9iXJUvKOXrwEFrt3IaLL7qA9h060btvf4758VBOOm4IXdq1okHDhtxwc3Ji9NDhJ3LaiT/lh7u1JyI4atCxtGlbedmADVlJSQmXFczjQek8/u3I8+lQMI+PH3YsHdJ5fFPBPD75tDPYf589kOfxcpUvCc688SXGnNuTmjXErU+9y5uzPufXP+rApOnzGDvhQx6fPJsD2pUy8YqDKV8S/PK28Xz69WI2qlWTxy/sA8BXC79l2NXPUr6kkuVk2yCp0r0F6ylJM0gyXfOBK4G9SILjGRHRL90Xdg+wAHgaODUi6knaFzgrIqqUTmrXoVM89PRL1fAObKm6G+U6Rs6F8vL8/NvOq5KaxbNPZX3WdPBfs+5CUVv05EiWfDYjN5O5XYdO8XA1fUeXNtxoYkR0Xt7z6V71q0gSPTdGxCUVnu9KEp/sChwZEXev7DVzlRGLiG0K7h5fyfPvkrz5pX6RPv4MyV4yMzMzy7kslhHT8ld/BLoDs4DxksZERGHRvg+AIcBZVT1urgIxMzMzs4zsBkyLiPcAJN0BHAR8F4hFxIz0uSqfFutAzMzMzHJF1Ve+oomkCQX3r4+I69PbpcCHBc/NAnZf0xd0IGZmZmaWmLeCPWKVRX9rvBnXgZiZmZnlSzanFswCtiq43xyYs5y2VZbXOmJmZmZm69J4oKWkbSXVBo4Exqzkd1bKgZiZmZnliqrpZ0Uiogw4haSW6ZvAXRHxhqSRkgYASOoiaRZwOHCdpDdW9l68NGlmZma5kWUV/Ih4CHiowmPnFdweT7JkWWXOiJmZmZllxBkxMzMzy5VqLF+xzjkjZmZmZpYRZ8TMzMwsX4onIeaMmJmZmVlWnBEzMzOzXCmihJgDMTMzM8uXrMpXVAcvTZqZmZllxBkxMzMzyxG5fIWZmZmZrTlnxMzMzCw3hPeImZmZmdla4EDMzMzMLCNemjQzM7Nc8dKkmZmZma0xZ8TMzMwsV1y+wszMzMzWmDNiZmZmlh8qrj1iDsTMzMwsN0RxXfTbS5NmZmZmGXFGzMzMzPKliFJizoiZmZmZZcQZMTMzM8sVl68wMzMzszXmjJiZmZnlistXmJmZmWWkiOIwL02amZmZZcUZMTMzM8uXIkqJOSNmZmZmlhFnxMzMzCxXXL7CzMzMzNaYM2JmZmaWG6K4ylcoIrLuw3pH0ifAzKz7sYqaAPOy7kSR8xhXP49x9fMYV7+8jXGLiPhB1p2oKkmPkIxxdZgXEb2q6diVciBWJCRNiIjOWfejmHmMq5/HuPp5jKufx9hWhfeImZmZmWXEgZiZmZlZRhyIFY/rs+7ABsBjXP08xtXPY1z9PMZWZd4jZmZmZpYRZ8TMzMzMMuJAzMzMzCwjDsTMzDYAUlICc+l/zWz94ECsCEjaQdKOWfejGFX80vKXWPWStGXWfShibQEiIjyPq4eDXVsdDsRyTImNgfOAdVoJeEMgSZGezSJpL0i+xLLtVfFKx3i0pEZZ96WYFAQFd0gaBQ7GqkPh5wXQJtPOWK44EMuxSCwCrgOOlLRT1n0qJgVB2MnAtZK2zrhLRUvSHsDPgHMi4lNJ/mxaSwqCg/bA9pJuXfq4g7G1p+Dz4kjg75LqeXytKvxhl1OS2ko6QFLTiHgBGAdskT5XM9veFQ9JfYCfAD0i4gNJO3p8q0Uz4GBgl6w7UkwKlspKIuJbYHegk4Ox6iFpP+BEYGBEfA34s8JWyoFYfvUDDgLuSbMJ9YARkmpGRHm2XcuvSr6UNgbGAntIuhB4GHhYUnVdcHaDIqmZpPoRMRo4AjhdUt+IWJJ13/KuwlLZ5pJapMFYB6CDg7E1VzhukmoBdYFWJH+8ERFlHltbGRd0zYGl/5DTD8ydgBLgg4j4StLhQBegFOgNDI6IsRU+hK0KKuwJOwz4GHgHuDW9/Q/gSeBvwI0R8UhWfS0GkgYCJwEfAf8CbgK6Ar8ERkbEfRl2r2hI+hnQHWgI3BkRl6dBwyvAjIg4ONMO5lSFz4vNgLKImJ9m0U8CxkbEnyq2NauoJOsO2MoV/GPvB1wOvAU0lnR5RIyS9CDQBPgW6EHyAeB/9KuoYJzPBgYCx0fER5L6R8Ti9Lm+QEuSwMFWk6RdgF+QzNffAQcCf4yIu9Ol34slvQDM81xeNRUChOHAgIjoJukvwEhJ9SJipKTdgaclNQPmepxXTcEYnwnsA9SRdF1EjJYUwHGSNo6IKzy2tiJemlyPSdpK0g3p7UbACODIiBgA3Aj0kdQxIhZGxIcRMQTYTVKL7Hqdb5JaA/0iYm9guqT9SZcZ0k24I4FBEfFBht3MrYJlms2B+4F9gY7A8DTD2yoi7gT2i4hP/AW2aioEYVsCE4HBkk4HGpBkHEdIujgivomIvSNijsd59Ug6EegPDAI+B0ZJGhIRDwN/BbpIapBlH23954zYeiwiPpR0Tbq3Y6akD4FtgEkRcbOknYGfA0cCSOoE/ACYn1mnc6aSJYPPSP6yvQn4BtgU2EdSbeAW4IWI+HDd97Ro7ESS0X2bZO4eDfSPiPcl9QdOlDQoIj7KspN5VRCEDSXZc3cIsBGwP3BuREyVNBrYX1KDiPg8u97mW/pHxXySz9/jgSApIzRWUnlE3CbpiYjw57GtkDNi66mCfWFTgBslvQFMARpJap82GwPMk7Q0oP6YJJMwb513OIcqZA96SuoM1AGOJfmA/WNEHA38D1AnIr50ELb6JLUEXpF0VUTMAp5Jf7pL6gVcDPwpIj7Nrpf5J2lvkiBsUEQsAL4CpgFHpJmx2sBhDsKqTokaBfdrp+WDbiX5Hu0N/E9EPA48BlySnoTiIMxWypv1c0LSP4A+JBuaa5P849+P5B///Vn2Le8kjSDZE/YEyV6l4yLinfS5E4CTSZaE38iul/mW7m88AphDEujeHhFnpSeb9AEWAQ/6RJNVJ2mziPgivb0LSfbr58CZ6TIvkg4iWQI+gGTvo+fyKkj31X2d3j4D2J5kX+6vgHnAH0gCsIbAjsDlETEno+5azjgQW89JqrH0VH5JtwN7Az8CtgXmRMRz/uJafUouDXVlRPSR9HtgO+AwktPQGwF/Bs6OiNcz7GauSapLUgLkDxHxgKSGwHjg7og4J21TJyIWeC6vmnTJvA9JYDAfaArcRlLaZidgVJqlWdq+TpolsyqSNAA4KCKGSRoEDAP6kiyx/z0izkn/mNueZA/eUQ50bVU4EMuBCsHYA0CtiOhV8TlbuYpf9JJakSw9ziApA3JYRCxKP3yfBxZGxMJMOlsk0rMgbyBZ6p2YPtYHuBO4JiJ+kWX/8k7SVsCDJAWdu6R7S3cgWS5rTXIW9YNZ9jGvJDUmmaenkyzxnklSvmY3kk36A5eeUZ22/y47aVZV3iOWAxGxZOn+hIjoDyySdOnS5zLtXI5U2BPWBCAi3gI2AQYDh6dB2E+Bc4AaDsJWn6RtJdWNpMDwG8DfJNVJn/4MuBLYT9I+mXWyOHxEMr4vAsOVVNGfBowGppOMcd0sO5hj3wBlwPnAFSRZx6XlVgZExGJJ50k6L23/ZTbdtDzzWZM5sTQYSwOvMUC39AO3LOu+5UGFIOxUYICkOcC5JNfq/Bi4T9JzJHuZjvFJD6tPUk+SLNizkt4DLiBZ6n1R0mPA4STLZxsD/mNiNUkaTJIFO1pSKXAJcClJ5qYxSab3Rm8aXz1pSZUnSQKx35CcOT0OuAhoIqkbyZmpR6XtvcRkq8xLkzkkaV/g44hwUdEqWhrEKqnmfgJwBnAe8ClwM8kX1uHAF8DEpZv1bdVJ6kJy8sPD6UP9SU4wOYtkSacJSfmKLYD/Aw6JiPcy6GruVLK0Xh94HRgTEacqqYP3a2ArkrIVh/hM3zWjpC5jS+AakjqCHwKnkJSraACc5T2ktiYciFlRk7QXsCgiJknqAFwGPBsRF6Z7l64gCRKujYjXsuxrMZC0EfAu8O+I6JI+1onkBIjGwHmRXK2gDfAXkjP4pmTW4ZxKS4F8HRFz02BsIvB0RByfLkMeCzzhPyjWnnQe30kS6N5FsrWnjveE2ZryHjErdp2BuyS1Bd4jub7e/pK6RUR5RJwG1AJ+mgYRtprSDeL1Sc4c21rSOQDpBv37SPaFNU6bzwL6OghbNWk9qx2B/yWpv7ZFRHxFUpriMEk3RcT8iLjWQdjalc7jQ0myuMdHxLcOwmxtcEbMilKFM01HkpxBdhTwAcmy5PYktayeS9tsERH/zqq/eaekKv5FwEySZcdnSfbTXBoRl6ZtNo0Ib2ZeRZWV9JDUGziGpCzIM2lm7AJgCLA7ydYFf7hXg/SPuoURMT3rvlhx8GZ9K0oFQdjxJEUWy4A7SM6O/CNwInCCkkuRvOAgbPVJ2oNkv1339Od6YCFJUHC3pJoRcbGDsNVTcJLJKSR/QNQjWR4Tyb7GrdJ6YtsAu3suVy/vB7O1zYGYFS1JuwM/I7mwdG2STeO3k5wVeT3Jxby9SXzNzQJOAtqT1FtqR3Im6rYkga8vpbOGlFxceiAwnKQsxTkRcYakANoCe6SPOQgzyxkvTVrRWLqEU/DfDiRnNB0jSSSlEm4jCRR6Ae95+WbtkfRbkiWxq9KyCqcDB6cFRl0xfxVUMpfPJ8nkHktyCaNDSMp+1EhrWdWKiG+z7LOZrR5nxKwoVPii35hkaexdoJ2kX0XEb4GFkl4EPgGWODBY66YCxyu5CH1/4NSlpRM81lVXYS63TOuwbQfcTVK89aCIKEuXKsslXUey9G5mOeRAzIpCwT6a44EfShpPUvi2P3CPpOYktcIGklyWxEs4a99DJLWrBpBs0n8p4/7kToXCw6eQZBXHAO8D/YA70iBsCMly8EG+uoZZvnlp0oqGpONINoiPIKnqPoVkr9LbJF9aNUkuguzNttVo6RUfvBy5+tJrnfYjKVPRA9gUaEWy33Es0AE4zkWdzfLPGTHLrQrZg1ZAC6AvyWn9XwDTSDIKV0bEyMw6uuEpBy9Hrq70UkXXkBRknS7pJpL6VQBzgKuAxa5hZVYcXNDVcqlCEHYSyUV4byC5bE7/iOhKUrl9F5LrStbLrLMbGAdgayYiZpPUuusl6ciIWExSeuUTks/sbxyEmRUPZ8QslyrsCfsJ6TX1JO0GtJBUi+TsyCnA5RHxdXa9NVs1ETFa0mLgYklExB2SbgHqppX0zaxIOBCz3JK0CUnF/HOBBZJOIMmIlQJPkeyrOSYiPs6ul2arJyLGSloCXC+pLCLuBhyEmRUZb9a3XJM0HDgB+BB4h+QSO02A+4HZDsIs7yR1B6ZHhIsPmxUhB2KWa5I2JtkHNj0iPpU0CBhKckHphdn2zszMbMUciFlRkFSDZK/YGcBRLlFhZmZ54D1iViw2JrnkyxER8WbWnTEzM6sKZ8SsaLiAqJmZ5Y0DMTMzM7OMuKCrmZmZWUYciJmZmZllxIGYmZmZWUYciJmZmZllxIGY2QZKUrmkyZJelzRKUp01ONa+kh5Mbw+QdM4K2jZIL9S+qq9xgaSzqvp4hTa3SDpsFV5rG0muRWdm1c6BmNmGa2FEtI+ItsA3JJeK+o4Sq/wZERFjIuKSFTRpAKxyIGZmVowciJkZwDhghzQT9Kaka4FJwFaSekh6SdKkNHNWD0BSL0lvSXoeOGTpgSQNkXRNensLSfdKmpL+7AVcAmyfZuMuS9udLWm8pNck/abgWL+S9LakJ4CdVvYmJB2XHmeKpHsqZPkOlDRO0juS+qXta0q6rOC1j1/TgTQzWxUOxMw2cJJKgN7A1PShnYBbI6IDMB84FzgwIjoCE4Az02t83gD0B/YBtlzO4a8Gno2IdkBH4A3gHJJrg7aPiLMl9QBaArsB7YFOkrpK6gQcCXQgCfS6VOHtjI6ILunrvQkMK3huG6Ab0Bf4c/oehgFfRESX9PjHSdq2Cq9jZrZW+BJHZhuuTSRNTm+PA/4CNANmRsTL6eN7AK2BFyQB1AZeAloB70fEuwCS/gYMr+Q19gd+DBAR5cAXkhpWaNMj/Xk1vV+PJDCrD9wbEQvS1xhThffUVtJFJMuf9YBHC567KyKWAO9Kei99Dz2AXQv2j22WvvY7VXgtM7M15kDMbMO1MCLaFz6QBlvzCx8CHo+Ioyq0aw+srctyCLg4Iq6r8BpnrMZr3AIMjIgpkoYA+xY8V/FYkb72qRFRGLAhaZtVfF0zs9XipUkzW5GXgb0l7QAgqY6kHYG3gG0lbZ+2O2o5v/8kcGL6uzUlbQp8RZLtWupRYGjB3rNSSZsDzwEHS9pEUn2SZdCVqQ/MlVQLOKbCc4dLqpH2eTvg7fS1T0zbI2lHSXWr8DpmZmuFM2JmtlwR8UmaWfqHpI3Sh8+NiHckDQfGSpoHPA+0reQQpwPXSxoGlAMnRsRLkl5Iy0M8nO4T2xl4Kc3IfQ0MiohJku4EJgMz46SiuwAAAG5JREFUSZZPV+bXwD/T9lNZNuB7G3gW2AI4ISIWSbqRZO/YJCUv/gkwsGqjY2a25nzRbzMzM7OMeGnSzMzMLCMOxMzMzMwy4kDMzMzMLCMOxMzMzMwy4kDMzMzMLCMOxMzMzMwy4kDMzMzMLCP/D/w/v+eCFPHLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc = LinearSVC(tol=1e-05)\n",
    "svc.fit(X_train_vect, y_train)\n",
    "\n",
    "ysvm_pred = svc.predict(X_test_vect)\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy_score(y_test, ysvm_pred) * 100))\n",
    "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, ysvm_pred, average='micro') * 100))\n",
    "print(\"\\nCOnfusion Matrix:\\n\", confusion_matrix(y_test, ysvm_pred))\n",
    "\n",
    "plot_confusion_matrix(y_test, ysvm_pred, classes=class_names, normalize=True, title='Normalized confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Saving the tf-idf + SVM Model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create pipeline with our tf-idf vectorizer and LinearSVC model\n",
    "svm_model = Pipeline([\n",
    "    ('tfidf', vect),\n",
    "    ('clf', svc),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "filename = 'models/tfidf_svm.sav'\n",
    "pickle.dump(svm_model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anger'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = pickle.load(open(filename, 'rb'))\n",
    "\n",
    "message = 'delivery was hour late and my pizza is cold!' \n",
    "model.predict([message])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
